6 Pressurepath

In this chapter, we will see what a pressurepath object is and how to use it to compute the altitude of the bird throughout its trajectory.

Let’s load the tag from the Great Reed Warbler (18LX) created in the advanced tutorial.

load("./data/interim/18LX.Rdata")

6.1 Timeseries at a single position

Before creating a full pressurepath, we start with the basic building block of a pressurepath, which is to retrieve the pressure timeseries from ERA5 at a single location with geopressure_timeseries.

geopressure_timeseries relies on the pressure timeseries entry point of GeoPressureAPI which return the timeseries of pressure at a given latitude and longitude.

Let’s start by retrieving the pressure at the known site of equipment, querying the same date as the first stationary period.

ts <- geopressure_timeseries(
  lat = tag$stap$known_lat[1],
  lon = tag$stap$known_lon[1],
  start_time = tag$stap$start[1],
  end_time = tag$stap$end[1],
  quiet = TRUE
)

We can compare the retrieved ERA5 pressure to the pressure measured on the Great Reed Warbler:

6.2 Pressurepath

pressurepath <- pressurepath_create(
  tag,
  path = path_most_likely,
  quiet = TRUE
)

Note that if a position on the path is over water, it is automatically moved to the closest point onshore as we use ERA5 Land.

plot_pressurepath(pressurepath)

6.3 Altitude

The main benefit of creating pressurepath is the ability to retrieve ERA5 variable along the the trajectory of the bird. One of them is altitude which can be directly plot with

plot_pressurepath(pressurepath, type = "altitude")

You can also retrieve the ground elevation with path2elevation() which allows us to compare the altitude of flight compare the elevation.

elevation <- path2elevation(path_most_likely,
  scale = tag$param$scale,
  sampling_scale = tag$param$scale * 2,
  percentile = c(10, 50, 90)
)

Note that because of the imprecision of the position, particularly during flight, it’s important to analyse with caution the relationship between flight altitude and ground elevation. path2elevation() aggregate the elevation accross a larger area defined by scale and return different percentile.

# Compute distance along the path for pressurepath (to be able to plot it with elevation)
lonlat <- data.frame(
  lon = pressurepath$lon,
  lat = pressurepath$lat
)
distance <- geosphere::distHaversine(tail(lonlat, -1), head(lonlat, -1))
pressurepath$distance <- c(0, cumsum(distance))

# Get also a point per stap_id
# exclude flight
pp <- pressurepath[pressurepath$stap_id == round(pressurepath$stap_id), ]
# compute average flight and distance
pp_stap <- merge(
  tag$stap,
  data.frame(
    stap_id = sapply(split(pp$stap_id, pp$stap_id), median),
    altitude = sapply(split(pp$altitude, pp$stap_id), \(x) round(mean(x), 1)),
    distance = sapply(split(pp$distance, pp$stap_id), \(x) round(mean(x), 1))
  )
)
pp_stap$duration <- stap2duration(pp_stap)

# Plot
p <- ggplot() +
  geom_line(data = elevation, aes(x = distance / 1000, y = X50, color = "ground")) +
  geom_line(data = pressurepath, aes(x = distance / 1000, y = altitude, color = "flight")) +
  geom_point(data = pp_stap, aes(x = distance / 1000, y = altitude, name = stap_id, color = "stap", size = duration^(0.25) * 6)) +
  theme_bw() +
  ylab("altitude/elevation (m a.s.l.)") +
  xlab("Distance along trajectory (km)") +
  scale_color_manual(
    values = c(ground = "brown", flight = "black", stap = "blue"),
    labels = c(ground = "Ground elevation (median over 0.25°)", flight = "Bird flight altitude", stap = "Stationary period")
  ) +
  guides(size = FALSE)

# Interactive plot
plotly::layout(plotly::ggplotly(p), legend = list(orientation = "h"))

6.4 Save

The graph object can become extremely big for such models, and it might not be recommended to save it. Check its size with format(object.size(graph), units = "MB").

save(
  tag,
  # graph,
  pressurepath,
  path_most_likely,
  path_simulation,
  marginal,
  file = "./data/interim/18LX.RData"
)